{"title":"大数据和集体智慧","authors":"M. Ivanović, Aleksandra Klašnja-Milićević","doi":"10.1504/ijes.2019.102430","DOIUrl":null,"url":null,"abstract":"Nowadays the creation and accumulation of big data is an unavoidable process in a wide range of situations and scenarios. Smart environments and diverse sources of sensors, as well as the content created by humans, contribute to the big data's enormous size and characteristics. To make sense of the data, analyse and use these data, more and more efficient algorithms are being developed constantly. Still, the effectiveness of these algorithms depends on the specific nature of big data: analogue, noisy, implicit, and ambiguous. At the same time, there is the unavoidable scientific area of collective intelligence. It represents the capability of interconnected intelligences to collectively and more efficiently solve concrete problems than each individual intelligence would be able to do on its own. The paper presents an overview of recent achievements in big data and collective intelligence research areas. At the end, the perspectives and challenges of the common directions of these two areas will be discussed.","PeriodicalId":412308,"journal":{"name":"Int. J. Embed. Syst.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Big data and collective intelligence\",\"authors\":\"M. Ivanović, Aleksandra Klašnja-Milićević\",\"doi\":\"10.1504/ijes.2019.102430\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays the creation and accumulation of big data is an unavoidable process in a wide range of situations and scenarios. Smart environments and diverse sources of sensors, as well as the content created by humans, contribute to the big data's enormous size and characteristics. To make sense of the data, analyse and use these data, more and more efficient algorithms are being developed constantly. Still, the effectiveness of these algorithms depends on the specific nature of big data: analogue, noisy, implicit, and ambiguous. At the same time, there is the unavoidable scientific area of collective intelligence. It represents the capability of interconnected intelligences to collectively and more efficiently solve concrete problems than each individual intelligence would be able to do on its own. The paper presents an overview of recent achievements in big data and collective intelligence research areas. At the end, the perspectives and challenges of the common directions of these two areas will be discussed.\",\"PeriodicalId\":412308,\"journal\":{\"name\":\"Int. J. Embed. Syst.\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Embed. Syst.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijes.2019.102430\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Embed. Syst.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijes.2019.102430","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nowadays the creation and accumulation of big data is an unavoidable process in a wide range of situations and scenarios. Smart environments and diverse sources of sensors, as well as the content created by humans, contribute to the big data's enormous size and characteristics. To make sense of the data, analyse and use these data, more and more efficient algorithms are being developed constantly. Still, the effectiveness of these algorithms depends on the specific nature of big data: analogue, noisy, implicit, and ambiguous. At the same time, there is the unavoidable scientific area of collective intelligence. It represents the capability of interconnected intelligences to collectively and more efficiently solve concrete problems than each individual intelligence would be able to do on its own. The paper presents an overview of recent achievements in big data and collective intelligence research areas. At the end, the perspectives and challenges of the common directions of these two areas will be discussed.